K_Means_Clustering_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___\n",
    "# K Means Clustering Project - Solutions\n",
    "\n",
    "For this project we will attempt to use KMeans Clustering to cluster Universities into to two groups, Private and Public.\n",
    "\n",
    "___\n",
    "It is **very important to note, we actually have the labels for this data set, but we will NOT use them for the KMeans clustering algorithm, since that is an unsupervised learning algorithm.** \n",
    "\n",
    "When using the Kmeans algorithm under normal circumstances, it is because you don't have labels. In this case we will use the labels to try to get an idea of how well the algorithm performed, but you won't usually do this for Kmeans, so the classification report and confusion matrix at the end of this project, don't truly make sense in a real world setting!.\n",
    "___\n",
    "\n",
    "## The Data\n",
    "\n",
    "We will use a data frame with 777 observations on the following 18 variables.\n",
    "* Private A factor with levels No and Yes indicating private or public university\n",
    "* Apps Number of applications received\n",
    "* Accept Number of applications accepted\n",
    "* Enroll Number of new students enrolled\n",
    "* Top10perc Pct. new students from top 10% of H.S. class\n",
    "* Top25perc Pct. new students from top 25% of H.S. class\n",
    "* F.Undergrad Number of fulltime undergraduates\n",
    "* P.Undergrad Number of parttime undergraduates\n",
    "* Outstate Out-of-state tuition\n",
    "* Room.Board Room and board costs\n",
    "* Books Estimated book costs\n",
    "* Personal Estimated personal spending\n",
    "* PhD Pct. of faculty with Ph.D.’s\n",
    "* Terminal Pct. of faculty with terminal degree\n",
    "* S.F.Ratio Student/faculty ratio\n",
    "* perc.alumni Pct. alumni who donate\n",
    "* Expend Instructional expenditure per student\n",
    "* Grad.Rate Graduation rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries\n",
    "\n",
    "** Import the libraries you usually use for data analysis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Read in the College_Data file using read_csv. Figure out how to set the first column as the index.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('College_Data',index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the head of the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abilene Christian University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>2885</td>\n",
       "      <td>537</td>\n",
       "      <td>7440</td>\n",
       "      <td>3300</td>\n",
       "      <td>450</td>\n",
       "      <td>2200</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12</td>\n",
       "      <td>7041</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adelphi University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>6450</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adrian College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
       "      <td>11250</td>\n",
       "      <td>3750</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agnes Scott College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>5450</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska Pacific University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Private  Apps  Accept  Enroll  Top10perc  \\\n",
       "Abilene Christian University     Yes  1660    1232     721         23   \n",
       "Adelphi University               Yes  2186    1924     512         16   \n",
       "Adrian College                   Yes  1428    1097     336         22   \n",
       "Agnes Scott College              Yes   417     349     137         60   \n",
       "Alaska Pacific University        Yes   193     146      55         16   \n",
       "\n",
       "                              Top25perc  F.Undergrad  P.Undergrad  Outstate  \\\n",
       "Abilene Christian University         52         2885          537      7440   \n",
       "Adelphi University                   29         2683         1227     12280   \n",
       "Adrian College                       50         1036           99     11250   \n",
       "Agnes Scott College                  89          510           63     12960   \n",
       "Alaska Pacific University            44          249          869      7560   \n",
       "\n",
       "                              Room.Board  Books  Personal  PhD  Terminal  \\\n",
       "Abilene Christian University        3300    450      2200   70        78   \n",
       "Adelphi University                  6450    750      1500   29        30   \n",
       "Adrian College                      3750    400      1165   53        66   \n",
       "Agnes Scott College                 5450    450       875   92        97   \n",
       "Alaska Pacific University           4120    800      1500   76        72   \n",
       "\n",
       "                              S.F.Ratio  perc.alumni  Expend  Grad.Rate  \n",
       "Abilene Christian University       18.1           12    7041         60  \n",
       "Adelphi University                 12.2           16   10527         56  \n",
       "Adrian College                     12.9           30    8735         54  \n",
       "Agnes Scott College                 7.7           37   19016         59  \n",
       "Alaska Pacific University          11.9            2   10922         15  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the info() and describe() methods on the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 777 entries, Abilene Christian University to York College of Pennsylvania\n",
      "Data columns (total 18 columns):\n",
      "Private        777 non-null object\n",
      "Apps           777 non-null int64\n",
      "Accept         777 non-null int64\n",
      "Enroll         777 non-null int64\n",
      "Top10perc      777 non-null int64\n",
      "Top25perc      777 non-null int64\n",
      "F.Undergrad    777 non-null int64\n",
      "P.Undergrad    777 non-null int64\n",
      "Outstate       777 non-null int64\n",
      "Room.Board     777 non-null int64\n",
      "Books          777 non-null int64\n",
      "Personal       777 non-null int64\n",
      "PhD            777 non-null int64\n",
      "Terminal       777 non-null int64\n",
      "S.F.Ratio      777 non-null float64\n",
      "perc.alumni    777 non-null int64\n",
      "Expend         777 non-null int64\n",
      "Grad.Rate      777 non-null int64\n",
      "dtypes: float64(1), int64(16), object(1)\n",
      "memory usage: 115.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>777.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3001.638353</td>\n",
       "      <td>2018.804376</td>\n",
       "      <td>779.972973</td>\n",
       "      <td>27.558559</td>\n",
       "      <td>55.796654</td>\n",
       "      <td>3699.907336</td>\n",
       "      <td>855.298584</td>\n",
       "      <td>10440.669241</td>\n",
       "      <td>4357.526384</td>\n",
       "      <td>549.380952</td>\n",
       "      <td>1340.642214</td>\n",
       "      <td>72.660232</td>\n",
       "      <td>79.702703</td>\n",
       "      <td>14.089704</td>\n",
       "      <td>22.743887</td>\n",
       "      <td>9660.171171</td>\n",
       "      <td>65.46332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3870.201484</td>\n",
       "      <td>2451.113971</td>\n",
       "      <td>929.176190</td>\n",
       "      <td>17.640364</td>\n",
       "      <td>19.804778</td>\n",
       "      <td>4850.420531</td>\n",
       "      <td>1522.431887</td>\n",
       "      <td>4023.016484</td>\n",
       "      <td>1096.696416</td>\n",
       "      <td>165.105360</td>\n",
       "      <td>677.071454</td>\n",
       "      <td>16.328155</td>\n",
       "      <td>14.722359</td>\n",
       "      <td>3.958349</td>\n",
       "      <td>12.391801</td>\n",
       "      <td>5221.768440</td>\n",
       "      <td>17.17771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2340.000000</td>\n",
       "      <td>1780.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3186.000000</td>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>776.000000</td>\n",
       "      <td>604.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>7320.000000</td>\n",
       "      <td>3597.000000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6751.000000</td>\n",
       "      <td>53.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1558.000000</td>\n",
       "      <td>1110.000000</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>1707.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>9990.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>8377.000000</td>\n",
       "      <td>65.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3624.000000</td>\n",
       "      <td>2424.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>4005.000000</td>\n",
       "      <td>967.000000</td>\n",
       "      <td>12925.000000</td>\n",
       "      <td>5050.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>1700.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>10830.000000</td>\n",
       "      <td>78.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48094.000000</td>\n",
       "      <td>26330.000000</td>\n",
       "      <td>6392.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>31643.000000</td>\n",
       "      <td>21836.000000</td>\n",
       "      <td>21700.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>2340.000000</td>\n",
       "      <td>6800.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>39.800000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>56233.000000</td>\n",
       "      <td>118.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Apps        Accept       Enroll   Top10perc   Top25perc  \\\n",
       "count    777.000000    777.000000   777.000000  777.000000  777.000000   \n",
       "mean    3001.638353   2018.804376   779.972973   27.558559   55.796654   \n",
       "std     3870.201484   2451.113971   929.176190   17.640364   19.804778   \n",
       "min       81.000000     72.000000    35.000000    1.000000    9.000000   \n",
       "25%      776.000000    604.000000   242.000000   15.000000   41.000000   \n",
       "50%     1558.000000   1110.000000   434.000000   23.000000   54.000000   \n",
       "75%     3624.000000   2424.000000   902.000000   35.000000   69.000000   \n",
       "max    48094.000000  26330.000000  6392.000000   96.000000  100.000000   \n",
       "\n",
       "        F.Undergrad   P.Undergrad      Outstate   Room.Board        Books  \\\n",
       "count    777.000000    777.000000    777.000000   777.000000   777.000000   \n",
       "mean    3699.907336    855.298584  10440.669241  4357.526384   549.380952   \n",
       "std     4850.420531   1522.431887   4023.016484  1096.696416   165.105360   \n",
       "min      139.000000      1.000000   2340.000000  1780.000000    96.000000   \n",
       "25%      992.000000     95.000000   7320.000000  3597.000000   470.000000   \n",
       "50%     1707.000000    353.000000   9990.000000  4200.000000   500.000000   \n",
       "75%     4005.000000    967.000000  12925.000000  5050.000000   600.000000   \n",
       "max    31643.000000  21836.000000  21700.000000  8124.000000  2340.000000   \n",
       "\n",
       "          Personal         PhD    Terminal   S.F.Ratio  perc.alumni  \\\n",
       "count   777.000000  777.000000  777.000000  777.000000   777.000000   \n",
       "mean   1340.642214   72.660232   79.702703   14.089704    22.743887   \n",
       "std     677.071454   16.328155   14.722359    3.958349    12.391801   \n",
       "min     250.000000    8.000000   24.000000    2.500000     0.000000   \n",
       "25%     850.000000   62.000000   71.000000   11.500000    13.000000   \n",
       "50%    1200.000000   75.000000   82.000000   13.600000    21.000000   \n",
       "75%    1700.000000   85.000000   92.000000   16.500000    31.000000   \n",
       "max    6800.000000  103.000000  100.000000   39.800000    64.000000   \n",
       "\n",
       "             Expend  Grad.Rate  \n",
       "count    777.000000  777.00000  \n",
       "mean    9660.171171   65.46332  \n",
       "std     5221.768440   17.17771  \n",
       "min     3186.000000   10.00000  \n",
       "25%     6751.000000   53.00000  \n",
       "50%     8377.000000   65.00000  \n",
       "75%    10830.000000   78.00000  \n",
       "max    56233.000000  118.00000  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA\n",
    "\n",
    "It's time to create some data visualizations!\n",
    "\n",
    "** Create a scatterplot of Grad.Rate versus Room.Board where the points are colored by the Private column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11db9da90>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VqA9qZGSkIWV3Su6jcxx/mOLZxgU+twvDm3tNG8JrlHI5joyM4A1veAOOznFM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\nO++87Dk333wzPvOZz9ge84c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jjjtw3333YXBwEN/61rfwpS99CW94wxsQDAbxwAMPYHx8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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11db91940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('Room.Board','Grad.Rate',data=df, hue='Private',\n",
    "           palette='coolwarm',size=6,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a scatterplot of F.Undergrad versus Outstate where the points are colored by the Private column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x144b90b38>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uFw5/O9Hx+kTH60NaUTPdrq7ATEDLly/H8uXL/a97e3sxffr0xJWMska4cbTR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\nXkLl8XikAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120839ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('Outstate','F.Undergrad',data=df, hue='Private',\n",
    "           palette='coolwarm',size=6,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a stacked histogram showing Out of State Tuition based on the Private column. Try doing this using [sns.FacetGrid](https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.FacetGrid.html). If that is too tricky, see if you can do it just by using two instances of pandas.plot(kind='hist'). **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L7e7u7qR1TFtqAwCMhREFrCS5+uqr09vbm7e97W3Zt2/f/tv37NmTuXPnDvv4rq45B9ch\nFDTZ52G9Pjv1B6qpTR/xr+6wWq3Hc9vdg1lae3GxMUfq53cP5ujjHk+tVm5/RqOzs5parTru9Wu1\nattqP6md9adq7eSJn/3ChbUJcyyaKH0wtZmHvNAM+wrz93//9+nu7s7v/d7vZfr06alUKnnFK16R\nrVu35tWvfnU2b96cU045ZdhCPT3OctFeXV1zJv087O0dzIz6UOr7hoqN2agPZdacBZm3YHGxMUdq\n1pz/l3p9KPV6uf0ZjUZjaNzr12rV1OtDban9VO2sP1VrJ0m9PpTe3npqtfYfi14Ix0QmP/OQiaB0\nyB82YL3+9a/PhRdemNWrV2doaCjr1q3LS17ykqxbty6NRiNLly7NqlWrijYFAAAwGQ0bsGbOnJk/\n+qM/etbtGzZsGJOGAAAAJiv/aBgAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsA\nAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKCQ\narsbAICpotlspru7u231Fy8+IpWK91YBxpKABQDjpL9vZ3oaSV9q4157d9/OnJpkyZIjx702wFQi\nYAHAOJp/2KJ0LT6qTdXrbaoLMHW4TgAAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQ\nAQsAAKAQAQsAAKAQ/2gYABhTzWYz3d0PtbWHxYuPSKUy/u8rT+V9h6lKwAIAxlR390O5+c7+LDhs\nUVvq7+7bmVOTLFly5LjXnsr7DlOVgAUAjLkFhy1K1+Kj2thBvW2Vp/K+w1TkfDEAAEAhAhYAAEAh\nLhFkzIzVB3tfiB/WHelz1d3dnSP27ctj+x4rVvvRx/al/+Ge9HTfX2zMJy3sWvKC+1kBTCZPvL50\nt63+C/E1G4YjYDFmursfyiP/9a9ZdNj8YmPu7OtPXvVbL7gP6470Q9B9uzozp1nJ4L5yL1b3PNib\nl1YfyXH9jxQbM0l27e5Pb97Y5s8dAExt/X0709NI+lIb99oW2GCqErAYU4sOm58juw4vOma5czcT\ny0g/BN3Z//PUps8oVrfaWcvCeXOzpPDPKUn6i48IwGjNb+siGxbYYOpxzhYAAKAQAQsAAKAQAQsA\nAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKCQ6oHuHBoa\nyic/+cncf//9aTQaOfvss3P88cdn7dq1qVQqWbZsWdavXz9evQIAAExoBwxY3/72t7NgwYJ89rOf\nzSOPPJI3v/nN+aVf+qWcd955Wb58edavX59NmzZl5cqV49UvAADAhHXAgPWGN7whq1atSpI8/vjj\nmTZtWnbs2JHly5cnSVasWJGbb75ZwAKACa7ZbKa7u3v/9/X67PT2Do5L7e7u7qR1zLjUei7P3Pfx\n1O59B8bfAQPWzJkzkySDg4P5yEc+knPPPTef+cxn9t8/a9asDAwMjKhQV9ecQ2iTyahen536A9XU\nph9wmo1KZ62aWQtnH/R8mqjzsF6fndruZmq1Az9XtVo1lUollUq5j092dHSkUukoOmaSVCqV1GrV\nA+5TZ2d12G3GUrvq12rVKbvvU7l2u+vvGehLf2NaBmsvSpL8fHczyYvGpfbP7x7M0cc93rbn/Zn7\nPp7ave/tnHO1WjULF9aGfe2dqK/NcLCG/W178MEH86EPfSirV6/OG9/4xnzuc5/bf9+ePXsyd+7c\nERXq6RlZEOOFo7d3MDPqQ6nvGyo2ZqM+lIHewdRqo59PXV1zJuw87O0dTL1eS71+4OeqXh9Ks9lM\ns9ksVrvVaqXZbBUdM3niHeN6feiA+9RoDA27zVhqR/1arZp6fWhK7vtUr93u+o3GUGbNWZh5CxYn\n+d+5OB5mzfl/bX/en7rv42ki7Hu76tfrQ+ntrR/wNXsivzYzdZQO+Qd8y3rXrl1Zs2ZNzj///Jxx\nxhlJkhNOOCHbtm1LkmzevDknnXRS0YYAAAAmqwOewfrSl76URx55JH/yJ3+Sa6+9Nh0dHbnoooty\n+eWXp9FoZOnSpfs/owUAADDVHTBgXXTRRbnooouedfuGDRvGrCEAAIDJqj2fuISDdCgrQR1oxazF\ni48ovsjDE70+NKJtu7u707erc9jt+nY9lEw71M7GR7PZfKLfA+jv68m+ej3Tp08f8bgLu5YU/1kB\nAJQiYDGp9OzuT2Xn1sx4dNGoH1t/oJoZz/Eh3519/cmrfitLlhxZosX9ursfyiP/9a9ZdNj8Ybc9\nYt++zGlW0tn/8wNud9e996Rx9JJSLY6pXf39OaZxc5ZUn/9D5UfOGsi0Snem9983sjF396c3b0zX\n4qNKtQkAUJSAxaSzaMG8HNl1+KgfV5tefd4VDR871Kaex6LD5o+o18f2PZbBfZXUps844HY7+/pK\ntTYuDl8wP0sOsP+DMzpTrXZmxsyRL53cX6IxAIAx4jobAACAQgQsAACAQlwiyJR3KAtnHEh3d3f+\nT/FRAQCYyAQsprxDWTjjQAZ/fk/qk2RBCgAAyhCwIAe/cMaBdPdOrgUpAAA4dD6DBQAAUIiABQAA\nUIiABQAAUIiABQAAUIiABQAAUIiABQAAUIiABQAAUIiABQAAUIiABQAAUIiABQAAUIiABQAAUIiA\nBQAAUIiABQAAUIiABQAAUIiABQAAUIiABQAAUIiABQAAUIiABQAAUIiABQAAUEi13Q3Ak1pJ9u17\n7IDb7KvXs69ey2PDbPdcmq1q6vWh5x2zlaRj1KMynprNZvp2PVRsvP6+nuyr13Pk0celUvF+EwBw\n6AQsJox9+x7L7sGhdHbWnnebvfVKpjc6Mrhv9H8MVxqtNJvPftzeeiXT9jayb99jmTF9xqjHZfzs\n6u/PMY2bs6S6uMh4R84ayO5H/m96e/5PuhYfVWRMAGBqE7CYUDo7a6kdIOR01mrDbvN8KpVKms3m\nc45Z7ewc9Xi0x+EL5mdJ1+FFxhqc0Zlp06q5r8hoAAA+gwUAAFCMgAUAAFCISwQhSdLKvn31oiOO\nZkGOJ2r7/BcAwGQnYEGSoUYjg/uSRsGTuqNZkGPvnkZmzHz+xT0AAJgcBCz4Hwe7eMbzjjeKBTnq\n9X3F6gIA0D4+gwUAAFCIgAUAAFCIgAUAAFCIgAUAAFCIgAUAAFCIgAUAAFCIgAUAAFCIgAUAAFCI\ngAUAAFCIgAUAAFDIiALWbbfdlne/+91JknvvvTdnnnlmVq9enUsvvXRMmwMAAJhMhg1Yf/7nf551\n69al0WgkSa666qqcd9552bhxY5rNZjZt2jTmTQIAAEwGwwasY489Ntdee+3+7++4444sX748SbJi\nxYps2bJl7LoDAACYRIYNWK973esybdq0/d+3Wq39X8+aNSsDAwNj0xkAAMAkUx3tAyqV/81ke/bs\nydy5c0f0uK6uOaMtxSRXr89O/YFqatNHNs2arWoqjdbT5tgzVToqqVQ6DrjNgTzX4yodlXR0NA9p\n3OesNYpen9hu+G0Pdf+fS0fHyGqP1kh6Hel+j2bMUfX4P/VrtWpqtVEfDg9arVZNZ2d13Os+VTvr\nT9Xa7a7/XLXHqw/P+9Tc91qtmoULa8P+DehvRF5oRv3b9rKXvSzbtm3LySefnM2bN+eUU04Z0eN6\nepzpmmp6ewczoz6U+r6hEW1frw+l2ayk2Ww+7zbNVjPNZuuA2zyfSuW5x262mmm1Wgc97vMZTa9P\nbDf8toey/89nLPY9GVmvI93v0Yw5qh7/p369PpR6fWTz9FDVatXU60NpNIbGte4ztbP+VK3d7vrP\nrP3kXGxH7fE2kZ73qVS/Xh9Kb289tdrz/w3Y1TXH34i0XemQP+qAdcEFF+Tiiy9Oo9HI0qVLs2rV\nqqINAQAATFYjClhHHXVUrrvuuiTJcccdlw0bNoxpUwAAAJORfzQMAABQiIAFAABQiIAFAABQiIAF\nAABQiIAFAABQiIAFAABQiIAFAABQiIAFAABQiIAFAABQiIAFAABQiIAFAABQSLXdDdB+zWYz3d0P\nFR+3u7s7/6f4qAAAMHEJWKS7+6E88l//mkWHzS867uDP70n96CVFxwQAgIlMwCJJsuiw+Tmy6/Ci\nY3b39hUdDwAAJjqfwQIAAChEwAIAAChEwAIAAChEwAIAAChEwAIAAChEwAIAAChEwAIAAChEwAIA\nACjEPxoGKKzZbKa358HnvK9Wq6ZeH0p/X0/21euZPn36qMbt6Eg6Og79vbGn1l/YtSSVivfbgLKa\nzWa6u7sPuE29Pju9vYNjUn/x4iMc22gLAQugsN6eBzP/7n/K4QvmP+u+SqWSZrOZI2cNZFqlO9P7\n7xvxuHf94p7MmzUzSxYvPuQen6w/cPf29OaN6Vp81CGPCfBU/X0709NI+lJ73m1qu5up15///oO1\nu29nTk2yZMmRxceG4QhYAGPg8AXzs6Tr8Gfd/mTAGpzRmWq1MzNmvmjEY+7s68uC2bOec9zRerL+\n9MG96T/k0QCe2/zDFh3wDZwnz+qPjfoYjQsH5rwpAABAIQIWAABAIS4RBJjCms1m+nY9VHxcC2cA\nMFUJWABT2K7+/hzTuDlLqoe+cMb+MXf3WzgDgClLwAKY4p5vQY5DYeEMAKYq128AAAAUImABAAAU\nImABAAAU4jNYk0y9Xs+tN383tc5yP7pdvb152eyx+id/AAAwdQhYk8yePYM5qmMgxy8ut+LXA5XO\nDPQPFBsPAACmKpcIAgAAFCJgAQAAFCJgAQAAFOIzWMCU1mw207froaJj9u16KJlWdMhJZbjntL+v\nJ/vq9UyfPn3UYy/sWpJKxXuDpTSbzfT2PFh8XD8nYCoTsIAprbf/4Rwz7eYsqZZbOOaue+9J4+gl\nxcabbHb19+eYxvM/p0fOGsi0Snem9983unF396c3b0zX4qNKtEmS3p4HM//uf8rhC+YXG9PPCZjq\nBCxgyjt8wfws6Tq82Hg7+/qKjTVZHeg5HZzRmWq1MzNmvmjU4/YfamM8S+n5n/g5AVOb8/cAAACF\nCFgAAACFuEQQAAo60MIRh7LAR2LxCBipZrOZ7u7utvawePERfl+nKAFrAvqHf/1+ps+a95z3DQ48\nkuP3PZJ5L5pZrF5v/2Aajz6Wnt2PpNV8PIsWLig2NsBUc6CFIw52gY/E4hEwGv19O9PTSPpSa0v9\n3X07c2qSJUuObEt92kvAmoA6ps/LkqW/9pz3PfJwX2r39mT6i8qt+FSbOZTmUCPTXzQ/j+3x0WSA\nQ/V8C0ccygIficUjYDTmH7aozW9I1NtYm3Y6qIDVarVyySWX5Kc//WlqtVquuOKKHHPMMaV7AwAA\nmFQO6sLQTZs2pV6v57rrrsvHPvaxXHXVVaX7AgAAmHQOKmDdcsstec1rXpMkeeUrX5nbb7+9aFMA\nAACT0UFdIjg4OJg5c+b87yDVaprNppVSCtndc38eb259zvv27h3ML/77/+aeB8utjNP38MM58kUd\naU3rTGuonj17B4qM+8DO3uzZ8+iIt99Xb+TRoUo6q53Pu83Onl157NG9B9VPpdKRZrP1nGNOrybT\nqp2p1Q5uZa/nMppeH907OKL6h7L/z6enry9DjXqmT59RdNyR9DrS/R7NmKPx6N7B7OzrS7PVLDZm\ncuA+n5yHo9334cYdrSfrj8WcGm7Mg9n35IlFHn6xc3v6dj100L3de89PM3PmnOwZ2H3QYwynv68n\ns1rdadT3Peu+g9335ND3/5n73tlZTU/3g8/b68Hatbs/fUNP7/Hh/l1pNBoHvXrioWpnfft+4Nq1\nWjX1+lBbao+l3X07k8XlPi/P5NLRarWe/RfnMK6++ur8yq/8SlatWpUkOf300/O9732vdG8AAACT\nykGdcnrVq16V73//+0mSW2+9NS996UuLNgUAADAZHdQZrKeuIpgkV111VV784hcXbw4AAGAyOaiA\nBQAAwLNZlQIAAKAQAQsAAKAQAQsAAKAQAQsAAKCQg/pHwyP11NUGa7VarrjiihxzzDFjWZIp6q1v\nfWtmz56dJDn66KNz9tlnZ+3atalUKlm2bFnWr1+fJPnGN76R66+/Pp2dnTn77LNz+umnZ9++fTn/\n/PPT29sf/DuiAAAJUklEQVSb2bNn5+qrr86CBQvauTtMMrfddluuueaabNiwIffee+8hz71bb701\nV155ZarVak499dR86EMfavMeMhk8dR7eeeedef/735/jjjsuSfKud70rb3jDG8xDxszQ0FA++clP\n5v7770+j0cjZZ5+d448/3vGQcfVc83DJkiXjfzxsjaF/+7d/a61du7bVarVat956a+ucc84Zy3JM\nUfv27WudccYZT7vt7LPPbm3btq3VarVan/rUp1rf+c53Wj09Pa03velNrUaj0RoYGGi96U1vatXr\n9dZXv/rV1he+8IVWq9Vq/dM//VPr8ssvH/d9YPL6yle+0nrTm97Uesc73tFqtcrMvTe/+c2t++67\nr9VqtVrve9/7WnfeeWcb9ozJ5Jnz8Bvf+Ebrq1/96tO2MQ8ZSzfccEPryiuvbLVardbDDz/cOv30\n0x0PGXdPnYf9/f2t008/vfXNb35z3I+HY3qJ4C233JLXvOY1SZJXvvKVuf3228eyHFPUXXfdlb17\n92bNmjV573vfm9tuuy07duzI8uXLkyQrVqzIzTffnJ/85Cc56aSTUq1WM3v27Bx33HG56667csst\nt2TFihX7t92yZUs7d4dJ5thjj8211167//s77rjjoOfef/7nf2ZwcDCNRiNHH310kuTXf/3Xc/PN\nN4//jjGpPNc8/N73vpfVq1dn3bp12bNnj3nImHrDG96Qj3zkI0mSxx9/PNOmTTuk12LzkIPx1HnY\nbDZTrVZzxx135Lvf/e64Hg/HNGANDg5mzpw5+7+vVqtpNptjWZIpaMaMGVmzZk3+4i/+Ipdcckk+\n/vGPp/WUf+82a9asDA4OZs+ePU+bjy960Yv23/7k5YVPbgsj9brXvS7Tpk3b//2hzL2BgYGn3fbU\n2+FAnjkPX/nKV+YTn/hENm7cmGOOOSZf/OIXn/WabB5S0syZM/fPqY985CM599xzHQ8Zd8+chx/9\n6Edz4okn5oILLhjX4+GYBqzZs2dnz549+79vNpupVKyrQVnHHXdcfvu3f3v/1/Pnz09vb+/++/fs\n2ZO5c+dm9uzZTwtPT739yXn6zAM/jNZTj3EHM/eeGfKf3BZGY+XKlXnZy162/+u77rorc+bMMQ8Z\nUw8++GDOOuusnHHGGXnjG9/oeEhbPHMetuN4OKZp51WvelW+//3vJ0luvfXWvPSlLx3LckxRN9xw\nQ66++uokSXd3dwYHB3Paaadl69atSZLNmzfnpJNOyi//8i/nlltuSb1ez8DAQH7xi19k2bJl+dVf\n/dX98/T73//+/ssZ4GC87GUvy7Zt25Ic3NybPXt2arVa7rvvvrRarfzgBz/ISSed1M5dYhJas2ZN\ntm/fniTZsmVLXv7yl5uHjKldu3ZlzZo1Of/883PGGWckSU444QTHQ8bVc83DdhwPO1pPPX9bWOsp\nqwgmyVVXXZUXv/jFY1WOKarRaOTCCy/MAw88kEqlkvPPPz/z58/PunXr0mg0snTp0lx++eXp6OjI\nN7/5zVx//fVptVo555xzsnLlyjz22GO54IIL0tPTk1qtlj/4gz/IwoUL271bTCL3339/Pvaxj+W6\n667LPffck4svvviQ5t5PfvKTXHHFFWk2mznttNPy0Y9+tN27yCTw1Hm4Y8eOfPrTn05nZ2e6urpy\n2WWXZdasWeYhY+aKK67Iv/zLv+QlL3lJWq1WOjo6ctFFF+Xyyy93PGTcPNc8PPfcc/PZz352XI+H\nYxqwAAAAphIfiAIAAChEwAIAAChEwAIAAChEwAIAAChEwAIAAChEwAIAAChEwAJgzO3duzeXXXZZ\nXv/61+ctb3lLVq9enS1bthzwMd/97nfzV3/1Vwfc5j3vec+wtb/whS/klltuGU27AHDQBCwAxtzZ\nZ5+dWq2Wf/7nf87f/d3f5aKLLsonPvGJbNu27Xkfc8cdd2RwcPCA427dunXY2lu3bk2z2Rx1zwBw\nMKrtbgCAF7atW7fmwQcfzNe+9rX9t51wwgn5wAc+kGuvvTaPP/54PvzhD+fkk0/O/fffn3e/+935\nyle+kuuuuy5JctRRR+WII47I5z73uVQqlcybNy/XXHNNrr322iTJO97xjlx//fXZuHFjvv3tb+fR\nRx9NpVLJH/7hH+YnP/lJbr/99qxbty5f/OIXM3369FxyySXp7+/PzJkzs27dupxwwglteV4AeGFy\nBguAMbV9+/a84hWveNbty5cvz/bt29PR0fG02zs6OrJ06dK8853vzDvf+c6cccYZ+dM//dNcdtll\n+du//dv8xm/8Ru68886sW7cuSXL99ddncHAwN910UzZu3Jh/+Id/yGtf+9r89V//dd7ylrfkFa94\nRa644oosW7YsF1xwQT7xiU/kxhtvzGWXXZZzzz13XJ4DAKYOZ7AAGFMdHR15/PHHn3V7o9EY8Riv\nfe1r88EPfjArV67Ma1/72px66qn7x06S2bNn55prrsk//uM/5p577sl//Md/PO3MVKvVyt69e7N9\n+/ZceOGFabVaSZLHHnssDz/8cObNm3couwgA+wlYAIypE088MRs3bszjjz+eadOm7b/9xz/+cU48\n8cQ0m839gWdoaOg5xzjrrLPym7/5m/nud7+bz33uc1m1alXe//7373/cQw89lHe/+91ZvXp1VqxY\nkcMPPzx33nnn08ZoNpuZMWNGvvWtb+2/rbu7W7gCoCiXCAIwppYvX57jjz8+V1555f4Adfvtt+fP\n/uzP8oEPfCALFizIz372syTJd77znf2PmzZt2v4zX7/zO7+TwcHBvOc978lZZ52VO+64I0lSrVbz\n+OOPZ/v27Tn22GNz1lln5cQTT8zmzZv3L2xRrVYzNDSU2bNn59hjj823v/3tJMkPf/jDrF69etye\nBwCmho7Wk2//AcAYqdfr+fznP5/vfe97qVarmTdvXj784Q/n137t17J9+/asXbs206dPz8qVK3PD\nDTfk3//93/OjH/0oa9euze/+7u9m6dKlueqqqzJt2rTMnDkzl112WZYuXZoPf/jDufvuu/ONb3wj\nH/zgB9Pd3Z3p06fnxBNPzM9+9rN8/etfz1/+5V/m+uuvz2c+85nMmzcvn/rUp/Lwww+nVqvl0ksv\nzctf/vJ2Pz0AvIAIWAAAAIW4RBAAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsAAKAQAQsA\nAKCQ/w8vQbEg/xDfkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x144b315c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"Private\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'Outstate',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a similar histogram for the Grad.Rate column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SqZTly5dn06ZNE9ErAADAlDZiwLr//vvT0NCQf/3Xf8327dvzN3/zNxkcHMy6deuyYsWK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\n1S3wQefXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f829780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"Private\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'Grad.Rate',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Notice how there seems to be a private school with a graduation rate of higher than 100%.What is the name of that school?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cazenovia College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>3847</td>\n",
       "      <td>3433</td>\n",
       "      <td>527</td>\n",
       "      <td>9</td>\n",
       "      <td>35</td>\n",
       "      <td>1010</td>\n",
       "      <td>12</td>\n",
       "      <td>9384</td>\n",
       "      <td>4840</td>\n",
       "      <td>600</td>\n",
       "      <td>500</td>\n",
       "      <td>22</td>\n",
       "      <td>47</td>\n",
       "      <td>14.3</td>\n",
       "      <td>20</td>\n",
       "      <td>7697</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Private  Apps  Accept  Enroll  Top10perc  Top25perc  \\\n",
       "Cazenovia College     Yes  3847    3433     527          9         35   \n",
       "\n",
       "                   F.Undergrad  P.Undergrad  Outstate  Room.Board  Books  \\\n",
       "Cazenovia College         1010           12      9384        4840    600   \n",
       "\n",
       "                   Personal  PhD  Terminal  S.F.Ratio  perc.alumni  Expend  \\\n",
       "Cazenovia College       500   22        47       14.3           20    7697   \n",
       "\n",
       "                   Grad.Rate  \n",
       "Cazenovia College        118  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Grad.Rate'] > 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Set that school's graduation rate to 100 so it makes sense. You may get a warning not an error) when doing this operation, so use dataframe operations or just re-do the histogram visualization to make sure it actually went through.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/marci/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "df['Grad.Rate']['Cazenovia College'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Private, Apps, Accept, Enroll, Top10perc, Top25perc, F.Undergrad, P.Undergrad, Outstate, Room.Board, Books, Personal, PhD, Terminal, S.F.Ratio, perc.alumni, Expend, Grad.Rate]\n",
       "Index: []"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Grad.Rate'] > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RLBYjynOGOwbAmHbk+8C+4Y5xQo2Nbxr0vgoWAMOiWieED8buJ16IWXOmD3cMgDGtWNwX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\n73437rzzzvj3f//3+MQnPhH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13687e588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"Private\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'Grad.Rate',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K Means Cluster Creation\n",
    "\n",
    "Now it is time to create the Cluster labels!\n",
    "\n",
    "** Import KMeans from SciKit Learn.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create an instance of a K Means model with 2 clusters.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fit the model to all the data except for the Private label.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=2, n_init=10,\n",
       "    n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,\n",
       "    verbose=0)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(df.drop('Private',axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the cluster center vectors?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.81323468e+03,   1.28716592e+03,   4.91044843e+02,\n",
       "          2.53094170e+01,   5.34708520e+01,   2.18854858e+03,\n",
       "          5.95458894e+02,   1.03957085e+04,   4.31136472e+03,\n",
       "          5.41982063e+02,   1.28033632e+03,   7.04424514e+01,\n",
       "          7.78251121e+01,   1.40997010e+01,   2.31748879e+01,\n",
       "          8.93204634e+03,   6.51195815e+01],\n",
       "       [  1.03631389e+04,   6.55089815e+03,   2.56972222e+03,\n",
       "          4.14907407e+01,   7.02037037e+01,   1.30619352e+04,\n",
       "          2.46486111e+03,   1.07191759e+04,   4.64347222e+03,\n",
       "          5.95212963e+02,   1.71420370e+03,   8.63981481e+01,\n",
       "          9.13333333e+01,   1.40277778e+01,   2.00740741e+01,\n",
       "          1.41705000e+04,   6.75925926e+01]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation\n",
    "\n",
    "There is no perfect way to evaluate clustering if you don't have the labels, however since this is just an exercise, we do have the labels, so we take advantage of this to evaluate our clusters, keep in mind, you usually won't have this luxury in the real world.\n",
    "\n",
    "** Create a new column for df called 'Cluster', which is a 1 for a Private school, and a 0 for a public school.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "def converter(cluster):\n",
    "    if cluster=='Yes':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Cluster'] = df['Private'].apply(converter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abilene Christian University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>2885</td>\n",
       "      <td>537</td>\n",
       "      <td>7440</td>\n",
       "      <td>3300</td>\n",
       "      <td>450</td>\n",
       "      <td>2200</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12</td>\n",
       "      <td>7041</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adelphi University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>6450</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adrian College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
       "      <td>11250</td>\n",
       "      <td>3750</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agnes Scott College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>5450</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska Pacific University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Private  Apps  Accept  Enroll  Top10perc  \\\n",
       "Abilene Christian University     Yes  1660    1232     721         23   \n",
       "Adelphi University               Yes  2186    1924     512         16   \n",
       "Adrian College                   Yes  1428    1097     336         22   \n",
       "Agnes Scott College              Yes   417     349     137         60   \n",
       "Alaska Pacific University        Yes   193     146      55         16   \n",
       "\n",
       "                              Top25perc  F.Undergrad  P.Undergrad  Outstate  \\\n",
       "Abilene Christian University         52         2885          537      7440   \n",
       "Adelphi University                   29         2683         1227     12280   \n",
       "Adrian College                       50         1036           99     11250   \n",
       "Agnes Scott College                  89          510           63     12960   \n",
       "Alaska Pacific University            44          249          869      7560   \n",
       "\n",
       "                              Room.Board  Books  Personal  PhD  Terminal  \\\n",
       "Abilene Christian University        3300    450      2200   70        78   \n",
       "Adelphi University                  6450    750      1500   29        30   \n",
       "Adrian College                      3750    400      1165   53        66   \n",
       "Agnes Scott College                 5450    450       875   92        97   \n",
       "Alaska Pacific University           4120    800      1500   76        72   \n",
       "\n",
       "                              S.F.Ratio  perc.alumni  Expend  Grad.Rate  \\\n",
       "Abilene Christian University       18.1           12    7041         60   \n",
       "Adelphi University                 12.2           16   10527         56   \n",
       "Adrian College                     12.9           30    8735         54   \n",
       "Agnes Scott College                 7.7           37   19016         59   \n",
       "Alaska Pacific University          11.9            2   10922         15   \n",
       "\n",
       "                              Cluster  \n",
       "Abilene Christian University        1  \n",
       "Adelphi University                  1  \n",
       "Adrian College                      1  \n",
       "Agnes Scott College                 1  \n",
       "Alaska Pacific University           1  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a confusion matrix and classification report to see how well the Kmeans clustering worked without being given any labels.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[138  74]\n",
      " [531  34]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.21      0.65      0.31       212\n",
      "          1       0.31      0.06      0.10       565\n",
      "\n",
      "avg / total       0.29      0.22      0.16       777\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "print(confusion_matrix(df['Cluster'],kmeans.labels_))\n",
    "print(classification_report(df['Cluster'],kmeans.labels_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not so bad considering the algorithm is purely using the features to cluster the universities into 2 distinct groups! Hopefully you can begin to see how K Means is useful for clustering un-labeled data!\n",
    "\n",
    "## Great Job!"
   ]
  }
 ],
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